# EE 422G - Signals and Systems Laboratory

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EE 422G
-

Signals and Systems Laboratory

Lab 1 Sampling and Quantization

Written by

Kevin D. Donohue

Department of Electrical and Computer Engineering

University of Kentucky

Lexington, KY 40506

January 17
, 20
1
3

Objectives:

Introduction to Matlab and Simulink features for signal analysis.

Apply Matlab to examine relationship between quantization bits and signal
-
to
-
noise
ratios (SNR).

Apply

to examine
aliasing and the impact of
non
-
ideal
low
-
pass filtering for
recons
tructing a signal from its samples.

1.
Back ground

This laboratory exercise focuses on

the relationship between
noise and interference

resulting
from
digitizing

an analog signal. Digitization results in 2 types of noise. The first
i
s from
sampling a continuous
-
time (CT) signal
at
discrete

points in
time
,

which

causes
aliasing
.
I
f
th
e
signal
is not
sampled
at a rate higher than twice its highest frequency, then interference
from aliasing may occur.

The second type of noise results f
rom round
ing
-
off the sample
amplitudes to discre
te levels.
This rounding error results in an additive noise process referred
to as
quantization noise
.

Sampling

and A
liasing
:

A
mathematical representation of the sampling
operation

is given by

a
sequence of

impulse
functions

in continuous time
:



n
nT
t
t
s
)
(
)
(

,

(1)

w
here the impulses are separated by
T

seconds (sampling interval)

corresponding

to a
sampling frequency of
T
F
s
1

Hz.
The sampling function
s
(
t
)
is shown in Fig. 1
.

Figure 1.

Impulse train to model sampling operation.

t

s
(
t
)

-
3
T

-
2
T

-
T

0
T

2
T

3
T

1

It can be shown that the
Fourier Transform of
s
(
t
)

is
also
an im
pulse train
in the frequency
domain with impulse
s

separated by the sampling frequency
:



k
s
kF
f
T
f
S
)
(
1
)
(
ˆ

(2)

This
impulse train
is illustrated in Fig. 2.

Figure

2
.

Fourier transform of sampling impulse train that creates aliased spectra
.

Now consider a CT signal,
x
c
(
t
), that is bandlimited to
N
F
:

N

for

0
F
f
f
X
c

(3
)

where
N
F
is referred to as the
bandlimit
.

As an example, consider t
he Fourier Transform
of

bandlimited signal

presented in Fig. 3.

Figure 3
.

Example of Fourier Transform of a bandlimited continuous
-
time signal.

Consider sampling

x
c
(
t
) by multiplying it with

the impulse train of Eq. (1), which

effectively
zeros out the infor
mation between sampli
ng points
:



n
c
s
nT
t
t
x
t
x
)
(
)
(
)
(

.

(4
)

Now f
or a
linear time
-
invariant (
LTI
) system, multiplication

in the time domain corresponds
to

convolution in the frequency domain
, given by
:

d
kF
f
X
T
kF
f
T
f
X
f
X
k
s
c
k
s
c
s





)
(
)
(
ˆ
1
)
(
1
)
(
ˆ
)
(
ˆ
.

(5
)

F
N

f

-
F
N

1

X
c
(
f
)

)
(
ˆ
f
S

-
3
F
s

-
2
F
s

-
F
s

0
F
s

2
F
s

3
F
s

f

T
1

Since the convolution of a signal with
a shifted delta Dirac function, results in a shift version
of the signal, E
quation (5
) shows

that sampling
replicates

the original
signal
spectrum along
the frequency axis separated by
integer multiples of
the sampling frequency
F
s
.
The
convolu
tion integr
al of Eq. (5) becomes
:



k
s
c
s
kF
f
X
T
f
X
)
(
ˆ
1
)
(
ˆ

(6)

The aliasing described in

Eq. (6)

is illustrated in Fig.

4

for two

case
s
. Figure 4a

shows the
case when
the sampling frequency is greater than

twice
N
F
.
Figure 4b shows

the case when
the sampling frequency is
less than

twice
N
F
.

The
Nyquist frequency
defined as half
of the sampling frequency of a digital processing
system
. This is also referred to as the folding frequency since frequencies beyond this

value
fold back onto the non
-
aliased spectral range.

Note that when the conditions on the sampling
theorem are met

(i.e. Nyquist frequency is greater than
F
N
)
,

the aliased spectra do not
interfere with each other and the original signal can be recovered
from its samples
with an
idea low
-
pass filter.

When overlap between aliased spectra occurs, the

interference
results in

an irreversible noise
/distortion

process and

the original signal cannot be recovered.

A
liasing
in this case
irreversibly

signal.

Bandlimit
F
N

is a property of the continuous
-
time signal and is sometimes referred to as the Nyquist rate.
Note:
The
Nyquist frequency is
a

property of the processing system while
Nyquist rate

is
a property

of the continuous
-
time
signal.

(a)

(b)

Figure 4
.

Aliased spectra when sampling rate is
(a)
greater than
twice the bandlimit;

N
s
F
F
2

, (b) less than twice the
bandlimit
,
N
s
F
F
2

.

X
s
(
f
)

F
S
-
F
N

f

F
S

-
2

F
S

-
F
S

2

F
S

3

F
S

-
3
F
S

F
N

f

-
F
N

X
s
(
f
)

F
S

-
2
F
S

-

F
S

2

F
S

The derivation leading to Fig. 4

is the basis for the sampling theorem, which states that a
bandlimited signal can be recovered from it samples if it is sampled at a rate greater than
twice its highest frequency.

The recovery can be performed with ideal low
-
pass filtering or
sinc functi
on interpolation.

Note in Fig. 4a an ideal low
-
pass filter can be applied with a cut
-
off anywhere between
F
N

and
F
S
-
F
N
. This way the original spectrum is undistorted and the
aliased
spectra are all eliminated. For the sampled signal of
Fig. 4b
,

this is not possible
because the original spectrum overlaps with the aliased spectra. No cutoff exists to eliminate
the aliased spectra without distorting the original spectrum.

Quantization and Uniformly Distributed Additive Noise.

A discrete
-
time signal cannot be stored on a computer, since it
has continuous amplitudes.
Therefore, th
e amplitude
s

must also be converted to discrete levels to
be
store
d

as binary
words
in
a
computer memory.

Figure 5 illustrates the quantization proces
s.

(a)

(b)

(c)

Figure 5.

(a) CT signal, (b) corresponding discrete
-
time

signal, (c)
digital

signal
quantized
to 4 levels corresponding to a 2 bit word per sample.

For or real signals t
he
error from round
-
off to the nearest quantization level is effectively a
random
noise process. The error can be modeled by subtracting

the distance from the
discrete
-
time signal amplitude to the nearest quantization level
, given by
:

)
(
)
(
)
(
nT
x
nT
x
nT
n
q
c
q

,

(7
)

w
here

x
q
(n
T
) is the signal value after roundi
ng off to a quantization level.

Noise processes are
modeled as random variables (RV)

with probability distribution
s

to
describe their amplitude variations
. If noise samples are uncorrelated from sample to sample,
the
process is called
white noise

and has a flat average spectrum (equal energy is expected at
a
ll frequencies). The noise amplitude values

are
modeled with
normal distribution
s for many
natural
processes
. However
,

quantization noise

is
typically modeled with a uniform
distribution,

since the amplitude
s

of
n
q

are expected with equal probability
between
the
quantization levels
.
Let

denote the distance between quantization levels. Then the
probability density function for
RV

n
q

is give
n

as:

)
(
t
x
c

)
(
nT
x
c

)
(
nT
x
q

Elsewhere

0
2
Δ
2
Δ
for

1
)
(
q
q
n
n
p

(8
)

Noise and signal relationships are often described in terms of their power ratios. For a noise
process mode
led as a
zero
-
mean
RV
, its

power is the same as
its

variance
. In the case of the
uniform distribution, the variance or power is given by

12
2
2

n

(9
)

a
nd the rms
value is equivalent to
its standard deviation (
i.e.
squ
are root of

ex
pression in

Eq.
(9
)).

In a classic derivation of the SNR for qua
ntization noise,
the signal is
assumed
to be

a

sinusoid with

amplitude

A

such that it extends

from the highest to

lowest

quantization levels.
Given the signal amplitude
extends to

the f
ul
l
-
scale range of the quantizer (2
A
),

can be

expressed
in terms of
A

for a specified number of bits
(
R
)
of
the computer word
used for
signal amplitude representations
.

The

SNR for a quantized full
-
range sinusoid

in dB is given
as:

dB

)
1
2
(
log
20
7609
.
1
)
(
10

R
q
R
SNR
.

(10
)

For large
R

(i.e. greater than 4)
, the minus 1 is ignored and

Eq. (10
) is often approximated as:

dB

6
7609
.
1
)
(
R
R
SNR
q

,

(11
)

s

to an oft
en
quoted
rule
-
of
-
thumb that 6dB SNR is gained
for each
bit
used to encode (qu
antize) the sampled

signal.

Note that the SNRq formula for Eq. (10) only
applies to a sinusoid that fills the full range of the quantizer. If the sinusoid does not extend
from the highest to lowest quantization level, then the SNR values will be less. T
his idea is
developed in one of the pre
-
lab exercises.

2.

Pre
-
L
ab
oratory

Assignment

1.

Consider quantizing the signal
)
2
cos(
8
)
(
t
t
x

Volts
.

a)

For one full period, sket
ch the results of quantizing

x
(
t
) uniformly with a 3 bit
word
, where the highest
quantization level

is set to
7
volts

and
the lowest level is
set to
-
7
volts
.

What

is

(from Eq. (8)) in this case
?

Assume a sampling rate of
10 where the first sample occurs at t=0, compute the mean and variance of the
quantization noise. Is this clos
e to what you would expect (see Eq. (9))? Why are
there differences?

b)

For one full period, sketch the results of quantizing

x
(
t
) with 1 bit, where one
quantization level is set to
-
4 volts and the other to 4 volts.

Would you expect to
hear/see quantization
noise

or distortion
if a signal was quantized in this way?

c)

Create a table where first column is the

number of quantization
bits and the
second column is

the expe
cted SNR
q

as computed from Eq. (10
)

for
R

from
1

to
16.

2.

Consider a
n 8
-
bit

quantizer

with a Full
-
Scale range from
-
8 to +8

volts.

a) Compute the SNRq for a non
-
aliased sinusoid with amplitude 1 volt. Describe
how and why this differs from the SNR
q

computed in the 8 bit table entry of Problem
1.

b) De
scribe what occur
s when a sinusoid with a10

volt

amplitude is presented to this
quantizer. Can
the overload error (clipping) best described as

noise

or distortion
?

3.

Consider a sy
stem with a sampling rate of
f
s

=
100
0

Hz
.
For the frequency range of 0
to
f
s
, sketch the DTFT magnitudes for each of the CT signals after sampling. Be sure
to explicitly label the frequency axis to denote the sinusoids position on the axis.

(a)

)
200
2
sin(
5
)
(
t
t
s

(b)

)
450
2
sin(
5
)
(
t
t
s

(c)

)
600
2
sin(
5
)
(
t
t
s

(d)

)
1250
2
sin(
5
)
(
t
t
s

(e)

)
2520
2
sin(
5
)
(
t
t
s

4.

Lab
oratory A
ssignment

This assignment

requires the
use
and modification of a Matlab
script

called
quant
_lab1.m

web
site
:
http://www.engr.uky.edu/~donohue/courses.html

Go to the EE422

mfile

save the
quant_lab1.m

mfile

file to your computer. Open this
file in Matlab’
s editor to study the comments and
modify
the code to do the exercises. This
the
EE422
data

Quantization

1.

Use the Matlab script
quant_lab1

to create a
2
-
second
sinusoid with uni
t amplitude,
frequen
cy
261.626

Hz, sampled at

16kHz
,

and quantize the signal

using
values of
“R” bits ranging from 2 to 16.

(a)

Design an experiment to determine the maximum SNR level required for noise to
be heard by humans.
Create a table with each entry

(row)

containing
the
number
of quantization bits
,
the theoretical SNR from Eq. (10), the experimental SNR
computed in the
quant_lab1

script, and

an indication of

whether
quantization
noise/distort
ion was heard

(use

the
soundsc

function in Matlab to play the
sounds)
.
Decide on

a consistent way to determine when the quantization noise is
heard, i.e. keep the volume constant for all trials, stay approximately the same
distance from the speakers, decide the
noise was heard if a majority (or all

or …
)
group members heard it.

Don’t

vary procedures between experiments
or
quantization bit values
or else you
will add unnecessary variability into the
comparisons and skew results.

(b)

Copy a

figure

of the average power spectra
for a case when the quantization
noise was clearly heard and past
e to a file so it can be put in the results section of

(c)

Describe the sound of the
quantization noise and indicate
if

the nature
/timbre

of
the noise

changes for decreasing number of quantization bits.

In the discussion
uestion,

“Is there a point at which you would no longer
considered quantization a noise process?”

(d)

Listen directly to the noise sequence produced by the 12 bit and 4 bit
quantization produced in part a. Describe the difference.
In the discussion
section
explain the reason for the difference in the nature of the sound.

Also
i
n the discussion section
,

compare the computed SNR
q

values to those
computed in the pre
-
laboratory exercises. Provide reasons for possible
discrepa
ncies. D
escribe how

quantization no
ise
appears in
the spectrum

(what does quantization noise look like in the frequency domain)
. Use the
figure of
part (
b
)

.

2.

Repeat
Exercise 1 using a sine wave with frequency 200 Hz.
In the discussion
section
, d
escribe and explain

observed

differences with part 1.

3.

Repeat Exercise 1 using wave file
mozart
-
1.wav
.

In the discussion
section,
d
escribe and explain

observed

differences with part 1.

In the discussion section for
preceding
Exercises 1,

2, and 3,
compare the 3 plots for
the average spectra and comment on differences
/similarities

observed for the
quantization noise and signals. Also comment on any difference
s
when the
qu
antization

noise became audible or changed

in nature
/timbre

(i.e. not just loud
er
noise, but a change in the way the original signal sounds).

Aliasing

prog
ram

by
either typing

at the

Matlab prompt, or
Library Browser
from the Matlab start menu button.

From the
L
ibrary
Browser
file menu, open a window for a new model.

From
the sub
-
libraries under

the Sig
nal Processing Blockset (Signal Operations, Signal Processing
Sinks, and Filtering)
populate the model space with the blocks as shown in Fig. 6.

Figure 6.

Simulink model of a sampling operation on a continuous
-
time signal using a
sample and hold

The sample
-
and
-
hold operation samples the signal
(from the signal generator
) on the rising
edge of the pulse generator and holds that value
constant

until the next sampling point

(
next
rising edge)
. So th
e pulse generator period is the

sampling interval for the analog
-
to
-
digital
sampled signal is plotted in
Time S
cope
, and the signal before
sampling is plotted in
Time S
cope1

for comparison. The FFT of the sampled signal is also
plotted
with (
Spectrum Scope
) to help identify

the signal frequency after sampling.

In order for the simulation to run properly,
parameters
need to be set

for each block and the
ove
r
all simulati
on. By double
clicking
each block element
,

a window

(dialog box)

appears

so that parameters can be set
. For the signal generator block
,

the frequency
and amplitude
must be set so
the proper sinusoid can be used to drive the system (Fig. 7a), the pulse
ge
nerator period must be set to achieve the desired
sampling rate (Fig. 7c), the FFT
(Spectrum Scope) parameters must be
configured
to define a window length over which the
FFTs are to be computed (Fig. 7b), and finally th
e simulation parameters
should
s
et

for
a

fixed step size (Fig. 7d). Typically

simulation

determines the time step
automatically based on the block settings
so the simulation is effectively continuous relative
to the timing

of the events generated by the blocks.

It must be set
for
fixed

time

so

the FFT
in
Spectrum Scope

can
sample data properly for its plots.

4.

Set the pulse generator to correspond to a sampling rate of
f
s

= 10
00

Hz. Set the
simulation time
(in
configuration parameters
)
to run from 0 to .1

seco
nds. This
window
is big enough to fit a single 10

Hz waveform. If a lower frequency is
generated, then a bigger simulation time must be set.
If there are too many periods
over

the simulation time
,

then it is hard to observe signal properties in the time

scope
windows.

So higher frequency waveforms
are

better observed in a smaller window.

There is a zoom on the graph window so the plots can be zoom in
on
if necessary.
Now set the signal generator

to

the frequenc
ies and amplitudes of those in pre
-
l
aborat
ory problem 3
(a) through (e). Copy the figures for the before and after time
waveforms as well as the Spectrum Scope for each case. Present these in your
results section
. In the discussion section describe key difference
s

between the
time signals before

and after sampling
,

and comment on diffe
rences between the
DTFT sketches in the pre
-
l
aboratory
a
ssignment

and
Spectrum Scope

output
.

5.

Now add an analog filter to the simulation to low
-
pass filter the output o
f the sample
-
and
-
hold operation as shown in
Fig. 8a.

Note the signal is analog after
it is low
-
pass
filtered,

so it must be resampled for the Spectrum Scope to process samples

and
display the FFT magnitudes
. Set the low
-
pass filter cut
-
off
(
or

edge
-
frequency) to a
value just less t
han half the samp
ling frequency

(i.e. 440

Hz) so the filter can roll
-
off
close to zero before crossing
beyond
the
half
-
sampling frequen
cy point. Now repeat
Exercise 4 with the low
-
pass filter processing the output.
In the dis
c
u
ss
ion

section
expla
in
differences observed

b
etween the filtered and non
-
filtered output
,

and
comment on the impact of the
(non
-
ideal)
low
-
pass filter in reconstructing a
sampled waveform.

(a)

(b)

(c)

(d)

Figure 7.

Dialog boxes for setting Simulink block and simulation parameters
(a) signal
generator (b) Spectrum Scope (FTT display) (c) Pulse Generator (d) overall simulation

simulation
)

(a)

(b)

Figure 8.